Application of ResUNet-CBAM in Thin-Section Image Segmentation of Rocks

Abstract

The convolutional neural network (CNN) algorithm in deep learning has been widely applied in petroleum geology research both domestically and internationally. Automated and accurate segmentation of thin-section images of rocks is foundational for in-depth analysis. However, traditional segmentation methods for reservoir rock thin sections often suffer from low accuracy and high cost. To address these issues, this paper proposes a novel segmentation algorithm based on an improved UNet network, integrating residual networks and the CBAM attention mechanism. By incorporating residual modules, the network depth is expanded, and the CBAM attention mechanism enhances the feature weighting capability during learning. Experimental results demonstrate that this method outperforms traditional approaches in both segmentation accuracy and efficiency, representing significant advancements in reservoir rock thin-section image segmentation.